Overview

Dataset statistics

Number of variables16
Number of observations1381
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.0 KiB
Average record size in memory51.9 B

Variable types

Numeric10
Categorical6

Alerts

Empresa is highly correlated with Annual Turnover and 5 other fieldsHigh correlation
Annual Turnover is highly correlated with Empresa and 5 other fieldsHigh correlation
Employee Count is highly correlated with Empresa and 5 other fieldsHigh correlation
Activos Fijos is highly correlated with Empresa and 5 other fieldsHigh correlation
Aon Office_cat is highly correlated with AonOffice_cat and 1 other fieldsHigh correlation
Industry_cat is highly correlated with IndustryCodeHigh correlation
TIER_cat is highly correlated with Empresa and 5 other fieldsHigh correlation
AonOffice_cat is highly correlated with Aon Office_cat and 1 other fieldsHigh correlation
AonOfficeCode is highly correlated with Aon Office_cat and 1 other fieldsHigh correlation
IndustryCode is highly correlated with Industry_catHigh correlation
TIERCode is highly correlated with Empresa and 5 other fieldsHigh correlation
TIERcode is highly correlated with Empresa and 5 other fieldsHigh correlation
Empresa is highly correlated with TIER_cat and 2 other fieldsHigh correlation
Annual Turnover is highly correlated with Activos FijosHigh correlation
Activos Fijos is highly correlated with Annual TurnoverHigh correlation
Aon Office_cat is highly correlated with AonOffice_cat and 1 other fieldsHigh correlation
Industry_cat is highly correlated with IndustryCodeHigh correlation
TIER_cat is highly correlated with Empresa and 2 other fieldsHigh correlation
AonOffice_cat is highly correlated with Aon Office_cat and 1 other fieldsHigh correlation
AonOfficeCode is highly correlated with Aon Office_cat and 1 other fieldsHigh correlation
IndustryCode is highly correlated with Industry_catHigh correlation
TIERCode is highly correlated with Empresa and 2 other fieldsHigh correlation
TIERcode is highly correlated with Empresa and 2 other fieldsHigh correlation
Empresa is highly correlated with Annual TurnoverHigh correlation
Annual Turnover is highly correlated with Empresa and 4 other fieldsHigh correlation
Employee Count is highly correlated with TIER_cat and 2 other fieldsHigh correlation
Activos Fijos is highly correlated with Annual Turnover and 3 other fieldsHigh correlation
Industry_cat is highly correlated with IndustryCodeHigh correlation
TIER_cat is highly correlated with Annual Turnover and 2 other fieldsHigh correlation
IndustryCode is highly correlated with Industry_catHigh correlation
TIERCode is highly correlated with Annual Turnover and 2 other fieldsHigh correlation
TIERcode is highly correlated with Annual Turnover and 2 other fieldsHigh correlation
TIER_cat is highly correlated with TIERCode and 2 other fieldsHigh correlation
TIERCode is highly correlated with TIER_cat and 2 other fieldsHigh correlation
TIER GENERAL is highly correlated with TIER_cat and 2 other fieldsHigh correlation
TIERcode is highly correlated with TIER_cat and 2 other fieldsHigh correlation
Empresa is highly correlated with TIER GENERAL and 3 other fieldsHigh correlation
Annual Turnover is highly correlated with Employee Count and 1 other fieldsHigh correlation
Employee Count is highly correlated with Annual TurnoverHigh correlation
Activos Fijos is highly correlated with Annual TurnoverHigh correlation
Aon Office is highly correlated with Aon Office_cat and 2 other fieldsHigh correlation
Industry is highly correlated with Industry_cat and 1 other fieldsHigh correlation
TIER GENERAL is highly correlated with Empresa and 3 other fieldsHigh correlation
Aon Office_cat is highly correlated with Aon Office and 2 other fieldsHigh correlation
Industry_cat is highly correlated with Industry and 1 other fieldsHigh correlation
TIER_cat is highly correlated with Empresa and 3 other fieldsHigh correlation
AonOffice_cat is highly correlated with Aon Office and 2 other fieldsHigh correlation
AonOfficeCode is highly correlated with Aon Office and 2 other fieldsHigh correlation
IndustryCode is highly correlated with Industry and 1 other fieldsHigh correlation
TIERCode is highly correlated with Empresa and 3 other fieldsHigh correlation
TIERcode is highly correlated with Empresa and 3 other fieldsHigh correlation
Activos Fijos is highly skewed (γ1 = 24.57726945) Skewed
Empresa has unique values Unique
Annual Turnover has unique values Unique
Activos Fijos has 44 (3.2%) zeros Zeros
Produccion has 14 (1.0%) zeros Zeros
Aon Office_cat has 77 (5.6%) zeros Zeros
Industry_cat has 15 (1.1%) zeros Zeros
AonOffice_cat has 77 (5.6%) zeros Zeros
AonOfficeCode has 77 (5.6%) zeros Zeros
IndustryCode has 15 (1.1%) zeros Zeros

Reproduction

Analysis started2022-09-10 00:10:06.312526
Analysis finished2022-09-10 00:11:12.449384
Duration1 minute and 6.14 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Empresa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1381
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean726.0246198
Minimum1
Maximum1753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-09-09T18:11:12.817370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70
Q1357
median719
Q31088
95-th percentile1410
Maximum1753
Range1752
Interquartile range (IQR)731

Descriptive statistics

Standard deviation429.3227454
Coefficient of variation (CV)0.5913335907
Kurtosis-1.153345119
Mean726.0246198
Median Absolute Deviation (MAD)366
Skewness0.06073991387
Sum1002640
Variance184318.0197
MonotonicityNot monotonic
2022-09-09T18:11:13.217663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1321
 
0.1%
2191
 
0.1%
6571
 
0.1%
10291
 
0.1%
9891
 
0.1%
5571
 
0.1%
5701
 
0.1%
9541
 
0.1%
5231
 
0.1%
1201
 
0.1%
Other values (1371)1371
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
17531
0.1%
17511
0.1%
15111
0.1%
14961
0.1%
14951
0.1%
14941
0.1%
14931
0.1%
14921
0.1%
14911
0.1%
14901
0.1%

Annual Turnover
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1381
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.171250896 × 1011
Minimum17990
Maximum6.2615849 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-09-09T18:11:13.615374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum17990
5-th percentile1130458656
Q11.7007349 × 1010
median6.3554511 × 1010
Q32.120179646 × 1011
95-th percentile1.273922 × 1012
Maximum6.2615849 × 1013
Range6.261584898 × 1013
Interquartile range (IQR)1.950106156 × 1011

Descriptive statistics

Standard deviation2.38142605 × 1012
Coefficient of variation (CV)5.709141238
Kurtosis425.7200292
Mean4.171250896 × 1011
Median Absolute Deviation (MAD)5.6658937 × 1010
Skewness18.64226381
Sum5.760497487 × 1014
Variance5.671190033 × 1024
MonotonicityNot monotonic
2022-09-09T18:11:13.991176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.792457353 × 10111
 
0.1%
3.94900026 × 10111
 
0.1%
1.082385079 × 10111
 
0.1%
2.0658045 × 10101
 
0.1%
3.2217318 × 10101
 
0.1%
3.46582179 × 10111
 
0.1%
2.870114518 × 10111
 
0.1%
7.525665158 × 10101
 
0.1%
5.912305 × 10101
 
0.1%
9.369707892 × 10111
 
0.1%
Other values (1371)1371
99.3%
ValueCountFrequency (%)
179901
0.1%
4437081
0.1%
10000001
0.1%
96241961
0.1%
125272411
0.1%
142070001
0.1%
251344811
0.1%
260931371
0.1%
353777081
0.1%
452960001
0.1%
ValueCountFrequency (%)
6.2615849 × 10131
0.1%
4.401029861 × 10131
0.1%
2.125358533 × 10131
0.1%
1.754272879 × 10131
0.1%
1.473396004 × 10131
0.1%
1.1021135 × 10131
0.1%
1.051657116 × 10131
0.1%
8.98888574 × 10121
0.1%
8.667597705 × 10121
0.1%
8.417604 × 10121
0.1%

Employee Count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct731
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean842.4221579
Minimum1
Maximum38622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-09-09T18:11:14.416999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q160
median210
Q3660
95-th percentile3477
Maximum38622
Range38621
Interquartile range (IQR)600

Descriptive statistics

Standard deviation2131.436433
Coefficient of variation (CV)2.530128645
Kurtosis93.81771377
Mean842.4221579
Median Absolute Deviation (MAD)188
Skewness7.670661804
Sum1163385
Variance4543021.267
MonotonicityNot monotonic
2022-09-09T18:11:14.808258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130
 
2.2%
215
 
1.1%
313
 
0.9%
513
 
0.9%
413
 
0.9%
1012
 
0.9%
10010
 
0.7%
459
 
0.7%
69
 
0.7%
539
 
0.7%
Other values (721)1248
90.4%
ValueCountFrequency (%)
130
2.2%
215
1.1%
313
0.9%
413
0.9%
513
0.9%
69
 
0.7%
77
 
0.5%
87
 
0.5%
94
 
0.3%
1012
 
0.9%
ValueCountFrequency (%)
386221
0.1%
230001
0.1%
204691
0.1%
170891
0.1%
145701
0.1%
134211
0.1%
130001
0.1%
127841
0.1%
123521
0.1%
114331
0.1%

Activos Fijos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1338
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.183332675 × 1011
Minimum0
Maximum1.07708124 × 1014
Zeros44
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-09-09T18:11:15.224220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18346000
Q13007724000
median1.7937413 × 1010
Q39.683993792 × 1010
95-th percentile1.191901918 × 1012
Maximum1.07708124 × 1014
Range1.07708124 × 1014
Interquartile range (IQR)9.383221392 × 1010

Descriptive statistics

Standard deviation3.409042978 × 1012
Coefficient of variation (CV)8.149108002
Kurtosis730.5028182
Mean4.183332675 × 1011
Median Absolute Deviation (MAD)1.7615085 × 1010
Skewness24.57726945
Sum5.777182424 × 1014
Variance1.162157402 × 1025
MonotonicityNot monotonic
2022-09-09T18:11:15.608184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044
 
3.2%
7.169544316 × 10111
 
0.1%
1.6697271 × 10101
 
0.1%
1.882094512 × 10111
 
0.1%
1.55887579 × 10111
 
0.1%
1.8778156 × 10101
 
0.1%
41466631391
 
0.1%
2.865878542 × 10101
 
0.1%
3.321499628 × 10101
 
0.1%
760490001
 
0.1%
Other values (1328)1328
96.2%
ValueCountFrequency (%)
044
3.2%
20001
 
0.1%
5480001
 
0.1%
11986211
 
0.1%
14017501
 
0.1%
14681661
 
0.1%
19369361
 
0.1%
22092721
 
0.1%
30440001
 
0.1%
40600001
 
0.1%
ValueCountFrequency (%)
1.07708124 × 10141
0.1%
4.257372232 × 10131
0.1%
1.8116425 × 10131
0.1%
1.7709473 × 10131
0.1%
1.759444784 × 10131
0.1%
1.5981658 × 10131
0.1%
1.341628877 × 10131
0.1%
1.309553558 × 10131
0.1%
1.1748621 × 10131
0.1%
1.104848718 × 10131
0.1%

Aon Office
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Bogota
848 
Cali
213 
Medellin
189 
Barranquilla
 
77
TBD
 
32

Length

Max length12
Median length6
Mean length6.325850833
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBogota
2nd rowBogota
3rd rowBogota
4th rowBogota
5th rowCali

Common Values

ValueCountFrequency (%)
Bogota848
61.4%
Cali213
 
15.4%
Medellin189
 
13.7%
Barranquilla77
 
5.6%
TBD32
 
2.3%
TBD Colombia22
 
1.6%

Length

2022-09-09T18:11:15.968153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-09T18:11:16.168133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
bogota848
60.4%
cali213
 
15.2%
medellin189
 
13.5%
barranquilla77
 
5.5%
tbd54
 
3.8%
colombia22
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Industry
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Retail and Wholesale Trade
165 
Food System, Agribusiness and Beverage
147 
Construction Services
125 
Manufacturing
112 
Energy
111 
Other values (14)
721 

Length

Max length38
Median length22
Mean length22.82621289
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFood System, Agribusiness and Beverage
2nd rowBusiness and Personal Services
3rd rowTransportation and Logistics
4th rowEnergy
5th rowPharmaceutical and Chemicals

Common Values

ValueCountFrequency (%)
Retail and Wholesale Trade165
11.9%
Food System, Agribusiness and Beverage147
10.6%
Construction Services125
9.1%
Manufacturing112
8.1%
Energy111
8.0%
Business and Personal Services107
7.7%
Professional Services101
7.3%
Financial Institutions97
 
7.0%
Technology and Communications94
 
6.8%
Pharmaceutical and Chemicals77
 
5.6%
Other values (9)245
17.7%

Length

2022-09-09T18:11:16.464103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and678
17.9%
services394
 
10.4%
retail165
 
4.3%
wholesale165
 
4.3%
trade165
 
4.3%
food147
 
3.9%
system147
 
3.9%
agribusiness147
 
3.9%
beverage147
 
3.9%
construction125
 
3.3%
Other values (26)1514
39.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TIER GENERAL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
TIER 4
689 
TIER 3
293 
TIER 1
235 
TIER 2
164 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTIER 1
2nd rowTIER 3
3rd rowTIER 3
4th rowTIER 1
5th rowTIER 2

Common Values

ValueCountFrequency (%)
TIER 4689
49.9%
TIER 3293
21.2%
TIER 1235
 
17.0%
TIER 2164
 
11.9%

Length

2022-09-09T18:11:17.016051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-09T18:11:17.202291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
tier1381
50.0%
4689
24.9%
3293
 
10.6%
1235
 
8.5%
2164
 
5.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Produccion
Real number (ℝ)

ZEROS

Distinct1366
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167595.525
Minimum-7258043.856
Maximum697114645.7
Zeros14
Zeros (%)1.0%
Negative23
Negative (%)1.7%
Memory size10.9 KiB
2022-09-09T18:11:17.474267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7258043.856
5-th percentile3061.22449
Q1172685.4286
median943433.7551
Q33197744.153
95-th percentile19100482.96
Maximum697114645.7
Range704372689.6
Interquartile range (IQR)3025058.724

Descriptive statistics

Standard deviation24070541.94
Coefficient of variation (CV)4.657977163
Kurtosis510.7242938
Mean5167595.525
Median Absolute Deviation (MAD)906459.3232
Skewness19.49226026
Sum7136449420
Variance5.793909894 × 1014
MonotonicityIncreasing
2022-09-09T18:11:17.863416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014
 
1.0%
3061.224492
 
0.1%
1224.4897962
 
0.1%
2110064.5851
 
0.1%
2167778.6991
 
0.1%
2167145.351
 
0.1%
2157843.2141
 
0.1%
2152662.2951
 
0.1%
2150200.7761
 
0.1%
2147742.6441
 
0.1%
Other values (1356)1356
98.2%
ValueCountFrequency (%)
-7258043.8561
0.1%
-4130887.9361
0.1%
-1822678.4611
0.1%
-1288362.1221
0.1%
-1242213.711
0.1%
-1240228.3571
0.1%
-378463.20921
0.1%
-358089.85721
0.1%
-287446.07141
0.1%
-249194.85711
0.1%
ValueCountFrequency (%)
697114645.71
0.1%
217903786.21
0.1%
213689703.11
0.1%
1968277941
0.1%
187723942.51
0.1%
175177926.31
0.1%
102263065.81
0.1%
1020054221
0.1%
97818904.611
0.1%
91935266.421
0.1%

Aon Office_cat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.505430847
Minimum0
Maximum5
Zeros77
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-09T18:11:18.167368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9829006425
Coefficient of variation (CV)0.6529032166
Kurtosis1.614981294
Mean1.505430847
Median Absolute Deviation (MAD)0
Skewness1.314584933
Sum2079
Variance0.9660936729
MonotonicityNot monotonic
2022-09-09T18:11:18.431347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1848
61.4%
2213
 
15.4%
3189
 
13.7%
077
 
5.6%
432
 
2.3%
522
 
1.6%
ValueCountFrequency (%)
077
 
5.6%
1848
61.4%
2213
 
15.4%
3189
 
13.7%
432
 
2.3%
522
 
1.6%
ValueCountFrequency (%)
522
 
1.6%
432
 
2.3%
3189
 
13.7%
2213
 
15.4%
1848
61.4%
077
 
5.6%

Industry_cat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.715423606
Minimum0
Maximum18
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-09T18:11:18.695321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q314
95-th percentile17
Maximum18
Range18
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.671129624
Coefficient of variation (CV)0.6507003997
Kurtosis-1.354557831
Mean8.715423606
Median Absolute Deviation (MAD)5
Skewness0.2289347205
Sum12036
Variance32.16171122
MonotonicityNot monotonic
2022-09-09T18:11:18.983298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
16165
11.9%
6147
10.6%
2125
9.1%
8112
8.1%
3111
8.0%
1107
7.7%
13101
7.3%
597
 
7.0%
1794
 
6.8%
1177
 
5.6%
Other values (9)245
17.7%
ValueCountFrequency (%)
015
 
1.1%
1107
7.7%
2125
9.1%
3111
8.0%
422
 
1.6%
597
7.0%
6147
10.6%
761
4.4%
8112
8.1%
914
 
1.0%
ValueCountFrequency (%)
1866
 
4.8%
1794
6.8%
16165
11.9%
1512
 
0.9%
1421
 
1.5%
13101
7.3%
1221
 
1.5%
1177
5.6%
1013
 
0.9%
914
 
1.0%

TIER_cat
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
3
689 
2
293 
0
235 
1
164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
3689
49.9%
2293
21.2%
0235
 
17.0%
1164
 
11.9%

Length

2022-09-09T18:11:19.287744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-09T18:11:19.471748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3689
49.9%
2293
21.2%
0235
 
17.0%
1164
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AonOffice_cat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.505430847
Minimum0
Maximum5
Zeros77
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-09T18:11:19.647708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9829006425
Coefficient of variation (CV)0.6529032166
Kurtosis1.614981294
Mean1.505430847
Median Absolute Deviation (MAD)0
Skewness1.314584933
Sum2079
Variance0.9660936729
MonotonicityNot monotonic
2022-09-09T18:11:19.913257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1848
61.4%
2213
 
15.4%
3189
 
13.7%
077
 
5.6%
432
 
2.3%
522
 
1.6%
ValueCountFrequency (%)
077
 
5.6%
1848
61.4%
2213
 
15.4%
3189
 
13.7%
432
 
2.3%
522
 
1.6%
ValueCountFrequency (%)
522
 
1.6%
432
 
2.3%
3189
 
13.7%
2213
 
15.4%
1848
61.4%
077
 
5.6%

AonOfficeCode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.505430847
Minimum0
Maximum5
Zeros77
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-09T18:11:20.153256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9829006425
Coefficient of variation (CV)0.6529032166
Kurtosis1.614981294
Mean1.505430847
Median Absolute Deviation (MAD)0
Skewness1.314584933
Sum2079
Variance0.9660936729
MonotonicityNot monotonic
2022-09-09T18:11:20.409216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1848
61.4%
2213
 
15.4%
3189
 
13.7%
077
 
5.6%
432
 
2.3%
522
 
1.6%
ValueCountFrequency (%)
077
 
5.6%
1848
61.4%
2213
 
15.4%
3189
 
13.7%
432
 
2.3%
522
 
1.6%
ValueCountFrequency (%)
522
 
1.6%
432
 
2.3%
3189
 
13.7%
2213
 
15.4%
1848
61.4%
077
 
5.6%

IndustryCode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.715423606
Minimum0
Maximum18
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-09-09T18:11:20.673201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q314
95-th percentile17
Maximum18
Range18
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.671129624
Coefficient of variation (CV)0.6507003997
Kurtosis-1.354557831
Mean8.715423606
Median Absolute Deviation (MAD)5
Skewness0.2289347205
Sum12036
Variance32.16171122
MonotonicityNot monotonic
2022-09-09T18:11:20.953183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
16165
11.9%
6147
10.6%
2125
9.1%
8112
8.1%
3111
8.0%
1107
7.7%
13101
7.3%
597
 
7.0%
1794
 
6.8%
1177
 
5.6%
Other values (9)245
17.7%
ValueCountFrequency (%)
015
 
1.1%
1107
7.7%
2125
9.1%
3111
8.0%
422
 
1.6%
597
7.0%
6147
10.6%
761
4.4%
8112
8.1%
914
 
1.0%
ValueCountFrequency (%)
1866
 
4.8%
1794
6.8%
16165
11.9%
1512
 
0.9%
1421
 
1.5%
13101
7.3%
1221
 
1.5%
1177
5.6%
1013
 
0.9%
914
 
1.0%

TIERCode
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
3
689 
2
293 
0
235 
1
164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
3689
49.9%
2293
21.2%
0235
 
17.0%
1164
 
11.9%

Length

2022-09-09T18:11:21.273620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-09T18:11:21.481581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3689
49.9%
2293
21.2%
0235
 
17.0%
1164
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TIERcode
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
3
689 
2
293 
0
235 
1
164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
3689
49.9%
2293
21.2%
0235
 
17.0%
1164
 
11.9%

Length

2022-09-09T18:11:21.745559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-09T18:11:21.937559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3689
49.9%
2293
21.2%
0235
 
17.0%
1164
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-09-09T18:11:06.690610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:35.310311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:38.884889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:42.191739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:46.034088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:49.424899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:52.895061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:56.470624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:00.090026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:03.357797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:07.375469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:35.874947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:39.228842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:42.543721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:46.378046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:49.769891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:53.227948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:56.833867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:00.426711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:03.692004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:08.008637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:36.210932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:39.556806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:42.895672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:46.722027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:50.126405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:53.567649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:57.201586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:00.753602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:04.027252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:08.406703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:36.578878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:39.909292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:43.263656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:47.082003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:50.496151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:53.919399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:57.607916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:01.102280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:04.375202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:08.756801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:36.899343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:40.245270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:43.607603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:47.409972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:50.828752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:54.245229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:57.953329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:01.426169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:04.708691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:09.121768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:37.243310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:40.581230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:43.975591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:47.745919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:51.184813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:54.584200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:58.301941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:01.759994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:05.047781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:09.458150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:37.563281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:40.885739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:44.327630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:48.075469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:51.510804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:54.900821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:58.642022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:02.059655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:05.365695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:09.806983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:37.900960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:41.221688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:44.706201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:48.411420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:51.870007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:55.243035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:59.002222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:02.401907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:05.717238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:10.140973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:38.220932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:41.541656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:45.042190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:48.739388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:52.194939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:55.553426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:59.343316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:02.708816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:06.032356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:10.475748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:38.548920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:41.847768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:45.666132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:49.070470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:52.536292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:55.859692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:10:59.699701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:03.024688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-09T18:11:06.351867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-09-09T18:11:22.185536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-09T18:11:22.737468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-09T18:11:23.273417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-09T18:11:23.769392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-09T18:11:24.171016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-09T18:11:11.167539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-09T18:11:12.040992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

EmpresaAnnual TurnoverEmployee CountActivos FijosAon OfficeIndustryTIER GENERALProduccionAon Office_catIndustry_catTIER_catAonOffice_catAonOfficeCodeIndustryCodeTIERCodeTIERcode
01325792457353193038716954431572BogotaFood System, Agribusiness and BeverageTIER 1-7258043.8560416011600
110616717610900097538508144000BogotaBusiness and Personal ServicesTIER 3-4130887.9359211211122
2622154255168000158787866173000BogotaTransportation and LogisticsTIER 3-1822678.461221182111822
31731271848954000157462891146000BogotaEnergyTIER 1-1288362.1224513011300
4144433259253705558129907322353CaliPharmaceutical and ChemicalsTIER 2-1242213.710202111221111
51318421555146528300039261MedellinPowerTIER 4-1240228.357143123331233
61112490236912000898450914685000BogotaManufacturingTIER 1-378463.2091818011800
761167091799500053144028890000BogotaEnergyTIER 1-358089.8571813011300
83048456090000009417709473000000MedellinManufacturingTIER 1-287446.0714338033800
956730794403000012319417683000BogotaRetail and Wholesale TradeTIER 2-249194.857141161111611

Last rows

EmpresaAnnual TurnoverEmployee CountActivos FijosAon OfficeIndustryTIER GENERALProduccionAon Office_catIndustry_catTIER_catAonOffice_catAonOfficeCodeIndustryCodeTIERCodeTIERcode
137147453545605299924706555499649859BogotaFood System, Agribusiness and BeverageTIER 191935266.4163316011600
13724548568763870005554120829242000BarranquillaPowerTIER 197818904.613500120001200
1373316877449463521244143590594585BogotaEnergyTIER 1102005422.0430513011300
1374307487108910001411184115035000BogotaEnergyTIER 1102263065.7581113011300
13752021077699948501521412903928190BogotaFinancial InstitutionsTIER 1175177926.2785715011500
137610522568657700062234931788938000BogotaEnergyTIER 1187723942.5326513011300
1377586675977050001082413416288766000BarranquillaAviationTIER 1196827793.9803200000000
1378244010298607360204695017659967440MedellinFinancial InstitutionsTIER 1213689703.1489835033500
1379729779187757407866379604155600BogotaFinancial InstitutionsTIER 1217903786.1775315011500
13809606168322000052246363713096000BogotaEnergyTIER 1697114645.7337513011300